This section introduces aspects that may help facilitate a better understanding of the disclosure. Accordingly, these statements are to be read in this light and are not to be understood as admissions about what is or is not prior art.
Currently in the practice of traffic congestion management there are typically humans in the loop that identify traffic problems using cameras or driver phone calls. The information is disseminated via means that include Traffic Message Signs, radio broadcasts, and TWITTER® feeds. This approach is time consuming and is limited to areas of the road network accessible by a costly Intelligent Transportation Systems infrastructure. A significant national concern is the frequency of fatal crashes due to distracted and inattentive drivers colliding into the back of a slowed or stopped queue. In recent years, crowd-sourced probe vehicle data has become commercially available, allowing for engineers and planners to assess traffic conditions on their road networks in real time. The data are provided as an average speed during a one minute interval over a predefined geometric segment of roadway. Using simple arithmetic, the difference in speed from one segment to the next, or delta speed, can then be calculated. This delta speed of average segment speeds is a good indicator if there is traffic congestion within a geometric segment. Using real time data to assess the road conditions and alert drivers will provide a quick and efficient way of preventing back of queue crashes. In the modern era of distracted driving new approaches are necessary to alert drivers before the critical decision point.
There is therefore an unmet need to use real-time data to identify locations with stopped or slowing traffic and alert drivers upstream using flashing lights, audible sirens, or display boards.